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Record W2891906689 · doi:10.1109/arith.2018.8464818

A Correctly Rounded Mixed-Radix Fused-Multiply-Add

2018· preprint· en· W2891906689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicNumerical Methods and Algorithms
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsRoundingComputer scienceArithmeticFloating pointRadix (gastropod)Double-precision floating-point formatParallel computingPoint (geometry)AlgorithmMathematicsOperating system

Abstract

fetched live from OpenAlex

The IEEE 754-2008 Standard governs Floating-Point Arithmetic in all types of Computer Systems. The Standard provides for two radices, 2 and 10. It specifies conversion operations between these radices, but does not allow floating-point formats of different radices to be mixed in computational operations. In contrast, the Standard does provide for mixing formats of one radix in one operation. In order to enhance the Standard and make it closed under all basic computational operations, we propose an algorithm for a correctly rounded mixed-radix Fused-Multiply-and-Add (FMA). Our algorithm takes any combination of IEEE754 binary64 and decimal64 numbers in argument and provides a result in IEEE754 binary64 and decimal64, rounded according to any for the five IEEE754 rounding modes. Our implementation does not require any dynamic memory allocation; its runtime can be bounded statically. We compare our implementation to a basic mixed-radix FMA implementation based on the GMP Multiple Precision library.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.302
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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